Quantitative Results. The quantitative results are listed in Table REF . To make a comprehensive comparison, we present the results on \(5\times 5\)  and \(9\times 9\)  LFs for \(2\times \)  SR and \(4\times \)  SR. It can be observed that our network achieves the best performance on 4 of 5 datasets (i.e. HCInew [1]}, HCIold [2]}, EPFL [3]} and INRIA [4]}). Moreover, compared to state-of-the-art LFSR methods ATO [5]} and LF-DFnet [6]}, our method improves the PSNR around 1 dB for \(2\times \)  SR, 0.6 dB for \(4\times \)  SR on small-disparity datasets (i.e., EPFL [3]}, INRIA [4]}), and around 0.3 dB for \(2\times \)  SR, 0.2 dB for \(4\times \)  SR on mid-disparity datasets (i.e., HCInew [1]} and HCIold [2]}), which demonstrate the effectiveness of our network to capture complementary information. However, our network performs less superior on the STFgantry [11]} dataset with large disparities. That is because, our network does not explicitly address the disparity issue and tries to cover the corresponding pixels by its large receptive field. As demenstrated in [6]}, it is difficult for network to incorporate the complementary information among different views without alignment of views when dealing with large disparities.
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